Associate Professor Kylie Walters received her Ph.D. in Biophysics from Harvard University. She joined the faculty of the University of Minnesota in 2002 and is in the Department of Biochemistry, Molecular Biology, and Biophysics. Professor Walters recently talked with an MSI staffer about her research and work with MSI.
MSI: How long have you been using MSI to support your research work?
Kylie Walters: I have been using MSI resources since I first came to the University of Minnesota, in 2002.
MSI: Could you talk a little about your research on ubiquitin signaling, what the goals of the research are?
KW: Ubiquitin signals are used for a large breadth of cellular events. Their most well-known use is to target proteins for degradation, including mis-folded or damaged proteins, or healthy ones that need to be removed under certain cellular conditions. Failure in ubiquitin signaling pathways is associated with cancer, neurodegenerative diseases, viral infection, and even diabetes.
MSI: How have MSI’s resources contributed to your research?
KW: My group uses structural biology tools to solve protein structures, characterize their dynamic behavior, and understand how they interact with each other. What we hope to obtain from this is a better understanding of protein signaling pathways, and ultimately to find new targets related to carcinogenesis and neurodegenerative diseases. Our most powerful technique is NMR spectroscopy, and we use MSI to process and analyze all of our NMR data. Specifically we use Itasca for our high-performance computational needs. Without Itasca some of our structural calculations would take a very long time. As we progress we try to characterize bigger and bigger complexes; consequently the calculation becomes larger as well. These calculations are greatly facilitated by parallel processing and supercomputing.
My group has also used MSI to help us procure various pieces of software. MSI also helped implement an on-site PDB (protein data bank). So now everything from the PDB is here in our lab. For example, we use a molecular modeling program called Rosetta. Rosetta is very good at taking a limited amount of information on a specific protein, then using the whole database of solved structures to come up with a model structure for that protein. This makes difficult proteins easier to analyze and you can access some structural data fairly quickly. Running Rosetta through the web can require greater than one month of waiting time, whereas, when we use MSI resources, we get results within a day.
MSI: How have your undergraduate and graduate students benefited from working with MSI?
KW: The facilities that MSI offers as well as their computer-savvy staff have been valuable resources in helping my graduate students to move their projects forward. I have also had undergraduate students participate in the MSI summer research program. The program gave the students a unique exposure to a much broader scope of research than in the lab. My students also go to the tutorials offered at MSI. The tutorials have been very useful.
MSI: Have any particular MSI staff members been critical to the success of your research efforts?
KW: Yuk Sham was very helpful in the area of molecular modeling when he was at MSI and we even co-authored a paper together [Editor’s note: Dr. Sham is currently Assistant Director of the Center for Drug Design as well as an MSI Principal Investigator]. My students have also worked with Nancy Rowe [Scientific Computing Consultant and Manager of the BSCL and BMSDL]. For example, graduate student Aaron Ehlinger worked with Nancy to get programs and software up and running. In many cases MSI will optimize source codes for software that we use in the BSCL.
MSI: How might MSI prepare to provide the cutting-edge support you’ll need going forward? For example, software applications or staff competencies we might add?
KW: I really feel like your support, software, and hardware have been fantastic. We are all around happy with our experience with MSI.
Description of Figure: Structure of proteasome ubiquitin receptor Rpn13 calculated by XPLOR-NIH version 2.24 in the MSI Basic Sciences Computing Laboratory. 347 paramagnetic relaxation enhancement distance constraints (dashed lines) were used to define inter-domain interactions between Rpn13's ubiquitin binding domain (orange) and its Uch37-binding domain (blue).